Pitch detection is of interest whenever a single quasi-periodic sound source is to be studied or modeled. For instance, the trajectory of a sound's pitch, also called the fundamental frequency, over a period of time can also be used to synthesize similar or related sounds using various speech or music synthesis techniques. An example of a quasi-periodic sound source is a singer's voice singing a particular note (e.g., high C). The sound generated by the singer typically has a certain amount of vibrato, or pitch modulation, noise and aperiodicity in the wave shape, making the sound quasi-periodic rather than a pure periodic signal.
Prior art pitch detection methods can be classified into three categories: frequency domain techniques, time domain techniques, and methods which use both techniques. The present invention is a time domain detection technique.
Important characteristics of a commercially useful pitch tracker are (1) real time operation on signals in the 0 to 5 KHz range using inexpensive hardware, (2) accuracy, and (3) generation of pitch values at regular (uniform) time intervals. Another useful characteristic is the ability to detect when the pitch tracker has lost track of the input signal, as may happen when the pitch of the input signal suddenly jumps, and to recapture the input signal without human intervention.
In time domain "feature detection" methods, the input signal is usually preprocessed to accentuate some time domain feature, and then the time between occurrences of that feature is calculated as the period of the signal. The pitch and period of the input signal are related by the equation: ##EQU1## A typical time domain feature detector is implemented by low pass filtering the signal, then detecting peaks or zero crossings of the filtered signal. Since the time between occurrences of a particular feature is used as the period estimate, feature detection schemes usually do not use all of the data available. Selection of a different feature often yields a different set of pitch estimates. Since estimates of the period are often defined at the instant when the features are detected, the frequency samples yielded are not uniformly distributed in time. To avoid the problem of non-uniform time sampling, a window of fixed size is moved through the signal, and a number of detected periods within each window are averaged to obtain the period estimate.